1. Field of the Invention
Aspects of the invention generally relate to a method and apparatus for substrate process inspection and monitoring.
2. Background of the Related Art
A chip manufacturing facility is composed of a broad spectrum of technologies. Cassettes containing semiconductor substrates are routed to various stations in the facility where they are either processed or inspected. Semiconductor processing generally involves the deposition of material onto and removal (“etching”) of material from substrates. Typical processes include chemical vapor deposition (CVD), physical vapor deposition (PVD), electroplating, chemical mechanical planorization (CMP), etching and others. During the processing and handling of substrates, the substrates undergo various structural and chemical changes. Illustrative changes include the thickness of layers disposed on the substrate, the material of layers formed on the substrate, surface morphology, changes in the device patterns, etc. These changes must be inspected and controlled in order to produce the desired electrical characteristics of the devices formed on the substrate. In the case of etching, for example, end-point detection methods are used to determine when the requisite amount of material has been removed from the substrate. More generally, successful processing requires ensuring the correct process recipe, controlling process deviations (e.g., gas flow, temperature, pressure, electromagnetic energy, duration, etc) and the like.
To process substrates effectively, the processing environment must be sufficiently stable and free from contamination. Sources of contamination include wear from mechanical motion, degradation of seals, contaminated gases, contaminated substrates, flaking of deposits from processing chamber components, nucleation of reactive gases, condensation during chamber pumpdown, arcing in plasma chambers and so forth. Such sources of contamination may produce particles that can contact the substrates and result in defective devices. As the geometries of device features shrink, the impact of contamination increases. Thus, current semiconductor manufacturing sequences routinely include inspection of substrates for particles and/or aberrations to identify “dirty” processes or equipment.
Currently, comprehensive testing and analysis of substrates for process integrity and contamination requires the periodic or often constant removal of one or more substrates from the processing environment into a testing environment. Thus, production flow is effectively disrupted during the transfer and inspection of the substrates. Consequently, conventional metrology inspection methods can drastically increase overhead time associated with chip manufacturing. Further, because such an inspection method is conducive only to periodic sampling due to the negative impact on throughput, some contaminated substrates may be processed without inspection resulting in fabrication of defective devices. Problems are compounded in cases where the substrates are re-distributed from a given batch making it difficult to trace back to the contaminating source.
Another disadvantage with conventional inspection systems is the prohibitive cost of the systems. Current systems are typically expensive, stand-alone platforms that occupy clean-room space. Due to the large area, or “footprint”, required by the stand-alone inspection platforms, the cost of owning and operating such systems is high. With regard to particle detection, the cost is further increased because of the electro-optics equipment utilized. This equipment is configured to produce high-resolution detection of small-scale particles and requires high-fidelity mechanisms, which are generally expensive to operate. Additionally, considerations of reduced throughput described above further increase the cost of conventional inspection systems.
One method to alleviate the throughput problems of conventional inspection systems is through in situ inspection. In situ inspection is often accomplished through the placement of inspection systems along the transfer paths of the substrates. In situ inspection acquires real-time data “on-the-fly” about the process and/or the substrates, while the substrates are moving between processes, thereby minimizing or eliminating the impact of the inspection on the process throughput. The inspection systems typically include receiving devices that gather sample images of the moving substrates that are sent to a data processing system for analysis. The image-gathering devices may be time-domain integration cameras (TDI), line cameras, charge coupled device cameras (CCD), and the like. An example of an exemplary in situ inspection system is described in U.S. patent application Ser. No. 09/680,226, entitled “Method and Apparatus For Enhanced Embedded Substrate Inspection Through Process Data Collection And Substrate Imaging Techniques,” filed on Oct. 6, 2000, and which is hereby incorporated herein by reference in its entirety.
Generally, in situ inspection systems require linear and/or synchronized linear substrate movements to accurately image a particular position coordinate, e.g., a location, on the moving substrate in order to detect and display on a monitoring system defects such as micron size particles, substrate surface conditions or aberrations, and the like. Although movements of the substrate transport system such as frog-leg and polar robots, conveyor belts, and other parts of the process system may include linear motion during substrate handling, many of the motions used to handle the substrate transportation are non-linear such as the acceleration and deceleration of the substrates as they are moved into and out of process chambers. Moreover, as the substrate is being moved from one process to the next, the linearity (e.g., smoothness) of the substrate motion is further influenced by system issues such as inertia, vibration, friction, and the like. Accordingly, depending upon the design and weight of the robot and substrate supporting surfaces, the number of available linear substrate movements required by the inspection process may be limited to specific portions of the substrate travel. For example, a frog-leg type of robot typically has two arms, each arm having a jointed arm section that is configured to allow the robot to extend and retract each arm when moving substrates into and from process chambers. The robot arms are typically driven by at least one motor such as a linear motor or stepper motor. During the extension or retraction of the substrate, the motor is accelerated or decelerated to extend the substrate or remove the substrate from a chamber or move a substrate along a transfer path. Typically, the motor has non-linear acceleration and deceleration movements as the motor is started and stopped. Furthermore, the robot arms are typically connected in such a way that the retraction and extension motion are usually non-linear with each rotational movement of the motor. Further, the robot generally includes a heavy blade on the extending end of the arms therefore increasing system inertia and vibration. Therefore, each rotation of the motor results in non-linear substrate movements through acceleration, deceleration, vibration, and fluctuations in velocity, which can affect the inspection process.
To resolve the issue of non-liner substrate movement during an in situ inspection process, carefully controlled synchronized imaging is often used to keep the inspection system synchronized with the substrate. Imaging synchronization generally refers to synchronizing the motion of the substrate with the imaging device, such as a line camera, so that the images are accurately acquired. For example, a conveyor belt system may include small imaging triggers, such as small optical trigger holes formed in the conveyor belt, magnetic devices, and other imaging triggers physically positioned to trigger the inspection system when it is time to acquire an image. Unfortunately, the imaging triggers are generally not physically small enough to allow for high-resolution imaging of the substrate and, under non-linear motion conditions between the trigger points may result in imaging distortions. Furthermore, due to the time delay (i.e., response bandwidth) between the imaging trigger and the actual image capture, the imaging trigger often limits the imaging system response. Therefore, to capture images accurately the substrate velocity is often slowed by the process system to accommodate the imaging system, degrading substrate-processing throughput.
Generally, the inspection system to properly acquire an image must generate and/or gather a considerable amount of light in order to focus and detect a defect or particle on the substrate surface as the substrate is moved through the process. Typically, the optical inspection system exposure is established by adjusting camera settings such as the aperture, exposure time, shutter speed, frame-rate, and the like, possibly impairing the image exposure and acquisition accuracy. For example, decreasing the shutter speed to obtain further exposure of a rapidly moving substrate surface area may blur the image, overexpose the slower moving portions of the substrate, and underexpose the more rapidly moving portions of the substrate. Unfortunately, improving the optical system response and sensitivity often requires increasing the cost of the equipment typically by adding more light sources, increasing the output intensity of the light sources, increasing the sensitivity of the receiving equipment, and the like. Therefore, improving system sensitivity often requires slowing the process, thereby decreasing throughput and increasing the cost of production.
FIGS. 1A-1I illustrate a substrate 28 under inspection being imaged, i.e., sampled, nine times at a constant rate by a receiver 58, such as a line camera. Each FIG. 1A-1I illustrates a single image position, i.e., the location on the substrate where the images 32A-I are acquired with respect to the center of the frog-leg robot 113. FIG. 1J illustrates the images 32A-I, or “image slices” of the substrate 28, as lines across the substrate surface. To detect micron size particles and aberrations on the surface of the substrate, each of the images 32A-I is typically narrow, less than 1 mm. For clarity, FIGS. 1A-1J represent only a fraction of the number of image positions required to completely image the substrate 28. As illustrated by FIG. 1J, the distance between each image 32A-I is variable.
FIG. 2 is a graph of the non-uniform distance imaging of the substrate 28 of FIG. 1 with respect to time. The y-axis represents the distance Y from the substrate center 52 to the frog-leg robot center 45. Further, the y-axis represents delta-Y, the distance between the image positions A-I. The x-axis represents the time from first image position A to the last image position 1. The velocity curve 205 illustrates the velocity change dv/dt (i.e., acceleration) of the blade center 52 during the imaging process due to the acceleration and deceleration of the substrate transport system. Additionally, curve 205 illustrates the velocity change between A and image position B is greater than the velocity change dv/dt between image positions H and I. The changing substrate velocity with respect to time results in a variation in delta-Y between the image positions.
FIG. 3 is an illustration of a distorted substrate output image 30 on a display 300 due to the non-linear imaging process illustrated by FIGS. 1 and 2. Generally, the display 300 is a linear device such as a monitor, television, and the like, where the screen is refreshed at a constant rate and requires a linear input to properly display an image. The x-axis and y-axis of the display 300 represent the distance from the center of the image, e.g., the substrate 28. The display 300 may be used to determine a coordinate of a particle and/or a defect on the substrate surface. For example, a particle at the center of the substrate 28 is 0,0. However, as the inspection system is acquiring the images at a constant rate from a non-linear system, the defect coordinate is inaccurate relative to the actual position on the substrate surface. For example, for an eight-inch diameter substrate 28 displayed on the display 300, the first image 32A is positioned at approximately minus four inches from the center of the substrate 28. Subsequent images 32B-I are displayed with a spacing of about 1 inch between each image. However, the actual spacing between the images are not uniform as indicated by FIGS. 1J and 2. For example, the delta-Y between image position A and B is about 2 inches. Therefore, as the acquired images 32A-I from the inspection system are displayed on the display 300, the actual distance between the images 32A-I changes relative to the constant refresh rate of the display 300, distorting the substrate image 30. Thus, the distorted image 30 causes inaccurate coordinate measurements of the substrate surface.
FIGS. 4A-G illustrate an in-situ inspection system where the receiver 58 is a time-domain integration (TDI) camera. The TDI camera may be used to increase the sensitivity for imaging moving substrates. The TDI camera operates in a similar way to other cameras, such as the line camera, except the TDI camera operates on the principle of integrating multiple exposures, i.e., multiple images, of the same subject, to increase the overall exposure of the subject. Typically, the TDI camera has several adjacent rows of light gathering sensors that image the same subject, as the subject passes beneath each sensor row. For example, FIG. 4C illustrates one TDI camera having four rows of sensors A-D representing 4096 bytes of information per row.
FIG. 4D illustrates an imaging sequence of a desired image position H corresponding to image 32H. The image sequence is set to an integration time T between each exposure. At the start of the sequence, sensor row A is given an image trigger signal by, for example, a controller, or user, and acquires the first image of image position H and sends the image data of image H to sensor set row B. At the end of the integration time T a second image of image position H is taken by sensor row B and is integrated with the previous image position H from sensor row A, and so on for each sensor row C and D. Unfortunately, to ensure that each sensor row (e.g., A-D) is identically aligned with the image position H, the conventional TDI camera typically requires that the moving substrate be synchronized with the integration time T and linear in movement. However, if the substrate image position H is not synchronized and aligned for each sensor row A-D, the resultant image is a composite of different images resulting in a defective and perhaps meaningless composite image output. For example, as illustrated by FIG. 4D, image position H is not aligned with each of the sensors A-D which may result in a distorted composite (i.e., integrated) image.
Therefore, there is a need for a method and apparatus for in-situ inspection and imaging of substrates in non-linear systems that provide accurate image results.